Corporations have made a significant shift toward increased globalization in the recent past. This is driven by many factors, from the need to be closer to global customers to workforce cost management. Communications technology has broken down many of the traditional barriers. As the corporations spread across the globe, they implement computer-based systems in each of their new locations. These systems typically require support by services organizations, which must accommodate for the growth of the corporations.
In the computer support services industry, knowledge is conventionally maintained by individual experts that are distributed globally in the service field. The geographically diverse experts use multiple information systems and a variety of analysis tools, making knowledge sharing very difficult.
The lifeblood of a services industry is the knowledge that it maintains. Support is offered on products based on the knowledge of the services engineers and the knowledge bases that support those services engineers. Knowledge is used to build training classes that are offered globally to customers to increase their effectiveness at operating their systems. Further, best practice architectures are built based on the knowledge and experience of architects and are offered as solutions to businesses.
The services industry has conventionally been a people intensive industry. As one would expect, the number of people required to service a technology is traditionally directly related to the complexity and market penetration of that technology. As technology complexity and product deployment has increased, as has the number of people employed by services organizations. In some industry examples, services organizations have outgrown the size of product development groups in the same technology corporation. Research into these cases reveals highly labor-intensive process-driven businesses with little direct implementation of technology to support the process.
Collecting and automating knowledge, such as by using decision trees, is not a new technology. In the 1980s, research was put into this by the expert system community. The focus of the research was on how the experts could be encouraged to divulge their knowledge into a computer system, and more importantly on how the knowledge could be refreshed and maintained. Experts, such as services engineers, are generally business critical and have not typically had the time to impart their knowledge. Even if they were allowed to do so, it was difficult to justify the ongoing knowledge refresh that the support system required. Additionally, under those conditions, the experts did not typically engage with the knowledge capture process.
The effect of automating knowledge of a subject matter expert had a direct and clear value to a business. This led to the growth of a cottage industry of software tools makers in the services industry. The vast majority of those tools were created in the spare time of the services engineers (the expert) with the subject matter expertise, and their requirements were usually founded in personal experience of repeated problems or customer concerns. This process grew and evolved through the 1990s as the services industry's tools space became globalized.
Much of the above issues apply to structured knowledge, but unstructured knowledge faces similar problems. Unstructured knowledge is conventionally gathered globally as documents into repositories. The large centralized repositories typically have little knowledgeable connections between their various documents and there is typically no concept of aging for the data. Efforts have been focused on creating meta data standards for documentation, which has improved some of the knowledge, however there is currently no single meta data standard for much of the knowledge.
Knowledge management is a technology that has held promise for many years now, often seen as a method of productivity increase based on the ability to capture knowledge for multi-purpose reuse. The services industry has segmented the knowledge management technology into structured and unstructured management systems. Structured knowledge systems focus on the application of well formatted data to problems or opportunities, while unstructured management systems focus on applications and creation of meta data systems and building or associating ontologies with them. Conventional knowledge management technologies, however, still suffer from the above-described problems.